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<p>Evidence theory is a framework for uncertainty reasoning that has made contributions in the field of artificial intelligence. There are two key issues in Evidence Theory: inability to find all inverse mass distributions from Pignistic Probability Transformation (PPT), and PPT may not be suitable for all sample distributions in the decision-making process. To address these key issues, Transfer Graph (TG) on Evidence Theory are proposed. Firstly, TG first constrains Mass Distributions with the same PPT results into a fixed dimensional space, solving the problem of being unable to find them. Secondly, by optimizing the Cross Entropy, Mass Transfer Learning (MTL) is proposed to achieve the optimal decision-making process. The experimental results indicate that MTL can optimize decision-making process to a certain extent, improve the accuracy of classification experiments.</p>

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Transfer Graph in the Context of Evidence Theory

  • 期刊论文

  • 发表日期：

    2025年7月4日

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  • Tianxiang Zhan,
  • Qianzhou Zhou,
  • Yuanpeng He,
  • Yong Deng

摘要

Evidence theory is a framework for uncertainty reasoning that has made contributions in the field of artificial intelligence. There are two key issues in Evidence Theory: inability to find all inverse mass distributions from Pignistic Probability Transformation (PPT), and PPT may not be suitable for all sample distributions in the decision-making process. To address these key issues, Transfer Graph (TG) on Evidence Theory are proposed. Firstly, TG first constrains Mass Distributions with the same PPT results into a fixed dimensional space, solving the problem of being unable to find them. Secondly, by optimizing the Cross Entropy, Mass Transfer Learning (MTL) is proposed to achieve the optimal decision-making process. The experimental results indicate that MTL can optimize decision-making process to a certain extent, improve the accuracy of classification experiments.